README.md

Introduction

DSCENet: Dynamic Screening and Clinical-Enhanced Multimodal Fusion for MPNs Subtype Classification is accepted by MICCAI 2024, Oral Presentation. You can access the full article here.

We propose a Dynamic Screening and Clinical-Enhanced Network (DSCENet) for the subtype classification of MPNs on the multimodal fusion of whole slide images (WSIs) and clinical information.

Usage

We have uploaded part of the code, in which the core model is located in Model.DSCE

        from Models.model_DSCE import DSCE
        model_dict = {
            "clinic_factor": args.clinic_factor,
            "n_classes": args.n_classes,
            "fusion": args.fusion,
        }
        model = DSCE(**model_dict)

Our code is currently undergoing generalizability testing. Stay tuned.

Availability of dataset

We are trying to communicate and make the feature of the data open source. For data usage and potential collaboration inquiries, please contact the author via email.

Citation

@InProceedings{Zha_DSCENet_MICCAI2024,
        author = { Zhang, Yuan and Qi, Yaolei and Qi, Xiaoming and Wei, Yongyue and Yang, Guanyu},
        title = { { DSCENet: Dynamic Screening and Clinical-Enhanced Multimodal Fusion for MPNs Subtype Classification } },
        booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
        year = {2024},
        publisher = {Springer Nature Switzerland},
        volume = {LNCS 15004},
        month = {October},
        page = {pending}
}